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1. A (mostly) symbolic system for monotonic inference with unscoped Episodic Logical Forms

   Gene Louis Kim, Mandar Juvekar ( June 2021 , NAtural LOgic Meets MAchine Learning (NALOMA'21))

   null (Ed.)

   We implement the formalization of natural logic-like monotonic inference using Unscoped Episodic Logical Forms (ULFs) by Kim et al. (2020). We demonstrate this system’s capacity to handle a variety of challenging semantic phenomena using the FraCaS dataset (Cooper et al., 1996).These results give empirical evidence for prior claims that ULF is an appropriate representation to mediate natural logic-like inferences. 

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2. Language-Model-Based Parsing and English Generation for Unscoped Episodic Logical Forms

   https://doi.org/10.32473/flairs.v35i.130703  

   Gibson, Erin ; Lawley, Lane ( May 2022 , The International FLAIRS Conference Proceedings)

   Unscoped Episodic Logical Forms (ULF) is a semantic representation for English sentences which captures semantic type structure, allows for linguistic inferences, and provides a basis for further resolution into Episodic Logic (EL). We present an application of pre-trained autoregressive language models to the task of rendering ULFs into English, and show that ULF's properties reduce the required training data volume for this approach when compared to AMR. We also show that the same system, when applied in reverse, performs well as an English-to-ULF parser. 

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3. Translating Omega-Regular Specifications to Average Objectives for Model-Free Reinforcement Learning

   https://doi.org/10.5555/3535850.3535933  

   Milad Kazemi ; Mateo Perez ; Fabio Somenzi ; Sadegh Soudjani ; Ashutosh Trivedi ; Alvaro Velasquez ( May 2022 , Proc. of the 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022),)

   Piotr Faliszewski ; Viviana Mascardi (Ed.)

   Recent success in reinforcement learning (RL) has brought renewed attention to the design of reward functions by which agent behavior is reinforced or deterred. Manually designing reward functions is tedious and error-prone. An alternative approach is to specify a formal, unambiguous logic requirement, which is automatically translated into a reward function to be learned from. Omega-regular languages, of which Linear Temporal Logic (LTL) is a subset, are a natural choice for specifying such requirements due to their use in verification and synthesis. However, current techniques based on omega-regular languages learn in an episodic manner whereby the environment is periodically reset to an initial state during learning. In some settings, this assumption is challenging or impossible to satisfy. Instead, in the continuing setting the agent explores the environment without resets over a single lifetime. This is a more natural setting for reasoning about omega-regular specifications defined over infinite traces of agent behavior. Optimizing the average reward instead of the usual discounted reward is more natural in this case due to the infinite-horizon objective that poses challenges to the convergence of discounted RL solutions. We restrict our attention to the omega-regular languages which correspond to absolute liveness specifications. These specifications cannot be invalidated by any finite prefix of agent behavior, in accordance with the spirit of a continuing problem. We propose a translation from absolute liveness omega-regular languages to an average reward objective for RL. Our reduction can be done on-the-fly, without full knowledge of the environment, thereby enabling the use of model-free RL algorithms. Additionally, we propose a reward structure that enables RL without episodic resetting in communicating MDPs, unlike previous approaches. We demonstrate empirically with various benchmarks that our proposed method of using average reward RL for continuing tasks defined by omega-regular specifications is more effective than competing approaches that leverage discounted RL. 

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4. Learning general event schemas with episodic logic

   Lawley, Lane ; Schubert, Lenhart ( July 2020 , Workshop at NASSLLI 2020, Brandeis University, Waltham MA, July 11-17, 2020.)

   null (Ed.)

   We present a system for learning generalized, stereotypical patterns of events—or “schemas”—from natural language stories, and applying them to make predictions about other stories. Our schemas are represented with Episodic Logic, a logical form that closely mirrors natural language. By beginning with a “head start” set of protoschemas—schemas that a 1- or 2-year-old child would likely know—we can obtain useful, general world knowledge with very few story examples—often only one or two. Learned schemas can be combined into more complex, composite schemas, and used to make predictions in other stories where only partial information is available. 

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5. Semantically informed data augmentation for unscoped episodic logical forms

   Juvekar, Mandar ; Kim, Gene ; Schubert, Lenhart ( June 2023 , 15th International Conference on Computational Semantics)

   Unscoped Logical Form (ULF) of Episodic Logic is a meaning representation format that captures the overall semantic type structure of natural language while leaving certain finer details, such as word sense and quantifier scope, underspecified for ease of parsing and annotation. While a learned parser exists to convert English to ULF, its performance is severely limited by the lack of a large dataset to train the system. We present a ULF dataset augmentation method that samples type-coherent ULF expressions using the ULF semantic type system and filters out samples corresponding to implausible English sentences using a pretrained language model. Our data augmentation method is configurable with parameters that trade off between plausibility of samples with sample novelty and augmentation size. We find that the best configuration of this augmentation method substantially improves parser performance beyond using the existing unaugmented dataset. 

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